Senior Engineer, Data Scientist - Nissan ISIT

Posted:
4/10/2026, 3:29:51 AM

Location(s):
Chennai, Tamil Nadu, India ⋅ Tamil Nadu, India

Experience Level(s):
Senior

Field(s):
AI & Machine Learning ⋅ Data & Analytics

Company

RNTBCI PL

Job Description

Qualifications:

  • 5+ years of experience delivering machine learning, AI, and analytics solutions.
  • Graduate in Computer Science, IT, AI/ML, Data Science, E&T or related field.
  • Experience in automotive and manufacturing domain (preferred).
  • Excellent stakeholder management and communication skills.

Key Tech Stack:

Cloud, AI & ML Platforms:
AWS (SageMaker, Bedrock, S3, ECS, Glue, Lambda, Step Functions, CloudWatch, CloudTrail)

Artificial Intelligence & Generative AI:

  • LLMs and multimodal models using Amazon Bedrock, SageMaker JumpStart, or custom NLP models
  • Prompt engineering, retrieval-augmented generation (RAG), embeddings, vector databases (OpenSearch/OpenSearch Serverless/PG Vector)

Machine Learning:
Regression, Classification, Time-Series Forecasting, Deep Learning (TensorFlow / PyTorch), NLP, Computer Vision, Feature Engineering, Model Monitoring, SageMaker Pipelines & Model Registry

Programming:
Python (mandatory), SQL

Version Control & CI/CD:
GitHub, GitLab, AWS CodeCommit/AWS CodePipeline

Security & Monitoring:
IAM, VPC security, key management (KMS), logging and model auditability using CloudWatch, SageMaker Model Monitor and Bedrock Guardrails

Other:
Cost optimization, performance tuning, responsible AI practices, MLOps frameworks.

Key Responsibilities:

  • Collaborate with business and IT teams to translate AI, ML, and analytical requirements into scalable solutions.
  • Build, train, fine-tune, and deploy ML and Generative AI models using AWS SageMaker and Amazon Bedrock.
  • Develop automated feature engineering and data pipelines to support ML and AI workloads on AWS.
  • Lead insights generation, data storytelling, and operational analytics using QuickSight or equivalent BI tools.
  • Implement responsible AI principles including model explainability, safety, fairness, and governance.
  • Define and enforce standards for ML lifecycle management, security, and compliance (GDPR, ISO, etc.).
  • Mentor and support teams including ML engineers, data engineers, and analytics developers.
  • Drive MLOps and GenAIOps adoption including CI/CD, automation, model monitoring, and operational excellence.
  • Evaluate emerging AWS GenAI capabilities and continuously adopt best practices (peer reviews, reusable templates, documentation).

Job Family

Information Technologies & Systems

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